标题： 用于识别纳米粒子超晶格扫描电子显微镜（SEM）图像中晶界的机器学习 显示英文标题

标题： Machine Learning for Identifying Grain Boundaries in Scanning Electron Microscopy (SEM) Images of Nanoparticle Superlattices

摘要： 由纳米颗粒有序排列组成的纳米颗粒超晶格表现出独特的光学、磁性和电子性质，这些性质源于纳米颗粒本身的特性以及它们的集体行为。 理解加工条件如何影响纳米尺度的排列和微观结构对于工程具有所需宏观特性的材料至关重要。 如晶界、晶格缺陷和孔隙等微观结构特征显著影响这些性质，但使用传统的人工分析方法难以量化，因为这些方法耗时且容易出错。 在本工作中，我们提出了一种机器学习工作流程，用于自动化扫描电子显微镜（SEM）图像中纳米颗粒超晶格的晶粒分割。 该工作流程将信号处理技术（如拉东变换）与无监督学习方法（如凝聚层次聚类）相结合，无需手动标注数据即可识别和分割晶粒。 在该工作流程中，我们将原始像素数据转换为可解释的超晶格取向数值表示以进行聚类。 基准测试结果表明，该工作流程对噪声图像和边缘情况具有鲁棒性，在标准计算硬件上每分钟可处理四张图像。 这种效率使该工作流程能够扩展到大规模数据集，并使其成为将数据驱动模型整合到材料设计和分析决策过程中的有价值工具。 例如，可以使用该工作流程在不同加工条件（如温度和压力）下量化晶粒尺寸分布，并利用这些知识调整加工条件以实现所需的超晶格取向和晶粒尺寸。 显示英文摘要

摘要： Nanoparticle superlattices consisting of ordered arrangements of nanoparticles exhibit unique optical, magnetic, and electronic properties arising from nanoparticle characteristics as well as their collective behaviors. Understanding how processing conditions influence the nanoscale arrangement and microstructure is critical for engineering materials with desired macroscopic properties. Microstructural features such as grain boundaries, lattice defects, and pores significantly affect these properties but are challenging to quantify using traditional manual analyses as they are labor-intensive and prone to errors. In this work, we present a machine learning workflow for automating grain segmentation in scanning electron microscopy (SEM) images of nanoparticle superlattices. This workflow integrates signal processing techniques, such as Radon transforms, with unsupervised learning methods like agglomerative hierarchical clustering to identify and segment grains without requiring manually annotated data. In the workflow we transform the raw pixel data into explainable numerical representation of superlattice orientations for clustering. Benchmarking results demonstrate the workflow's robustness against noisy images and edge cases, with a processing speed of four images per minute on standard computational hardware. This efficiency makes the workflow scalable to large datasets and makes it a valuable tool for integrating data-driven models into decision-making processes for material design and analysis. For example, one can use this workflow to quantify grain size distributions at varying processing conditions like temperature and pressure and using that knowledge adjust processing conditions to achieve desired superlattice orientations and grain sizes.